Ensemble space inversion (ENSI) enables efficient, regularisation-constrained calibration of complex, highly-parameterised models. This tutorial demonstrates how linear analysis can be undertaken in partnership with the ENSI calibration. This provides estimates of parameter and predictive uncertainty at minimal numerical cost. This tutorial is a continuation of another GMDSI tutorial. These tutorials can be done independently or […]
ENSI and Linear Analysis Read More »
Ensemble space inversion (ENSI) is implemented through the PEST_HP suite (version 18). Using ENSI you can calibrate a complex model quickly. The calibration subspace is comprised of random parameter realisations as well as individual parameters. Realisations can be different for different parameter types. Regularisation seeks a minimum error variance solution within the confines of the working
Ensemble Space Inversion Read More »
Building and history-matching a three-dimensional model is a difficult procedure. The third dimension increases parameter requirements, model run times, and model output uncertainty. Ideally, predictive uncertainty can be reduced through assimilating information from site characterisation on the one hand, and measurements of system behaviour on the other hand. But this is so much harder in
From Site Concepts to a 3D Site Model Read More »
Description Where: EcoSciences Precinct, Dutton Park, Brisbane When: Monday 3rd June to Friday 7th June, 2024 Who should attend: Both new and experienced modellers will benefit from the course, as well as anyone who would simply like to understand the theory and practice behind simulation-based data processing and environmental forecasting. Topic: “PEST” refers to a software package and
PEST Course: Brisbane Read More »
This tutorial explores the use of “conceptual points” as a precursor to model parameterisation. Expected hydraulic properties are provided at these conceptual points. Just as importantly, so-called “hyperparameters” are also ascribed to these points; these are used to characterise the nature of hydraulic property heterogeneity as it is likely to prevail in their vicinity. All
Conceptual Model to Numerical Model Read More »
This tutorial explores construction of the interface between PEST/PEST++and a simple MODFLOW/MODPATH model, and how to then subject that model to history-matching and uncertainty analysis–including data space inversion (DSI).There is some overlap with a previous tutorial. However, there are also some important differences. Use of the PLPROC, TPL2PST and PESTPREP2 utilities are explained. The first
Data Assimilation for a Simple Model Read More »
Week 1 NZ/AUS https://vimeo.com/866196071%20 This is the first recording from the NZ/AUS self-paced guided study course on Decision Support Groundwater Modeling with Python. Week 1 covered the Freyberg model, Bayes equation, and the PEST interface.Presenters: Jeremy White, Rui Hugman and Brioch Hemmings USA https://vimeo.com/866312617%20 This is the first recording from the USA self-paced guided study
Live session recordings Read More »
GMDSI and the USGS have co-funded the development of a series of jupyter notebooks that use python scripting to demonstrate many aspects of applied decision-support groundwater modelling, from introductions to concepts through to complete modelling workflows. This webinar presented and discussed the design and content of these notebooks, including how to get started using them,
Webinar: Applied decision support groundwater modelling with python Read More »
Using the PLPROC parameter preprocessor supplied with the PEST suite, moveable polylinear and polygonal structural features such as faults and aquitard windows can be inserted into a model. These features can be assigned to one or many model layers. Hydraulic properties can vary along and within them. If appropriate, the hydraulic properties associated with structural
Structural Overlay Parameters and PLPROC Read More »
Construction of a decision support groundwater model requires that parameters be adjustable, stochastic and representative of geological conditions. This can be difficult to achieve in complex hydrogeological environments where controls on hydraulic properties are both geological and structural in origin.This worked example report demonstrates use of the PLPROC parameter preprocessor in construction of a stochastic
Worked Example: Structural Overlay Parameters in a Tunnel Model Read More »
GMDSI is managed by the National Centre for Groundwater Research and Training (NCGRT) and administered by Flinders University.
